Μάθημα : ΓΡΑΜΜΙΚΑ ΜΟΝΤΕΛΑ

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# Lab 1: Introduction ----
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# This script includes the basics of R. No former experience on programming or
# statistics is required.

# You can use R or Rstudio, whichever you prefer.

# To run current line or selection in R: Ctrl+R
# To run current line or selection in Rstudio: Ctrl+Enter

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Basic Algebra ----
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~

# You can use R as a calculator:
3 + 6
3 - 6
3 * 6
3 / 6
3 ^ 2

# Generally, it is better to assign your values into a variable, which R will
# remember for future use.
a <- 3
b <- 6
a + b

# The name of a variable should be short and clear.
year <- 2000 # good name
year_of_new_millenium <- 2000 # too long
yonm <- 2000 # short, but unclear

# Check if two variables are equal.
x <- 3
y <- 5

x == y
x != y

# See all your variables.
ls()

# Delete a variable. (rm comes from "remove")
rm(a)
ls()

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Functions ----
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Create a variable
x <- 3

# Square root
sqrt(x)

# Logarithm (attention, for R this is ln, not log)
log(x)

# Exponential
exp(x)
exp(log(x))

# Absolute
abs(-5)

# Get info about a function
?sqrt
?exp

# We can create a function of our own
my_sum <- function(x, y) {
z <- x + y
z
}

my_sum(2, 3)

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Vectors ----
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~

# R can also work with vectors (dianysmata) and matrices (pinakes). R views
# vectors as a column.

# Create a vector
x <- c(2, 4, 6, -8)
x
y <- c(3, -5, 7, 11)
y

# Basic algebra with vectors
x + 2
2 * y
x ^ 2
1 / y

# Create a sequence of numbers
a <- 1:20
a
b <- 15:1
b

# Create a more complex sequence
seq(0, 10, by = 0.5)
seq(0, 5, length = 30)

# Repeat a number or vector
rep(0, times = 10)
rep(c(1, 2, 3), times = 3)
rep(c(1, 2, 3), each = 10)

# Get the third value of x
x[3]

# Get the 1st and the 3rd value of y
y[c(1, 3)]

# Get x without the 2nd and 3rd value
x[-c(2, 3)]

# Keep the values of x that are less than 3
x[x < 3]

# Find the position of these elements
which(x < 3)

# Keep the values of y between 1 and 10
y[y > 1 & y < 10]

# Find out if x and y are identical
identical(x, y)

# Sum the elements of x
sum(x)

# Find the length of y
n <- length(y)
n

# Transpose (anastrofos) - The vector is now a row
t(x)

# Multiply each element of x with an element of y
x * y

# Multiply the two vectors as usual (esoteriko ginomeno)
t(x) %*% y

# Multiply x (a column) with t(y) (a row)
x %*% t(y)

# Attention!
# x*y is a vector
# t(x)%*%y is a numeric
# x%*%t(y) is a matrix

# You can simply type x%*%y instead of t(x)%*%y and R will print the same
# (esoteriko ginomeno)

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Matrices ----
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~

# There are four primary ways to create a matrix in R

# 1: Use "rbind" to stack two vectors by row
A <- rbind(c(1, 2, 3), c(4, 5, 6))
A

# 2: Use "cbind" to stack two vectors by column
B <- cbind(c(1, 3), c(2, 4))
B

# 3: Use "matrix" to break a long vector into columns
C <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3)
C

# 4: Use "matrix" to break a long vector into rows
D <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3, byrow = TRUE)
D

# Index a matrix: get first column of A
A[, 1]

# Index a matrix: get first row of B
B[1, ]

# Index a matrix: get the element (1,2) of C
C[1, 2]

# Basic Algebra with the elements of a matrix
2 * A
A ^ 2
A + C

# Transpose of a matrix
t(A)

# Find the determinant (orizousa) of a matrix
det(B)
det(C) #why does this not work?

# Find out if which matrices are identical
identical(A, D)
identical(C, D)

# Matrix Multiplication
B %*% A
B %*% B
C %*% t(D)

# Eigenvalues, eigenvectors (idiotimes, idiodianysmata)
eigen(B)

# Inverse matrix (antistrofos)
solve(B)

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Random Samples ----
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Simulate n draws from N(mu, sd), the Normal distribution with mean equal to mu
# and standard deviation equal to sd.

n <- 100
mu <- 0
sd <- 1
x <- rnorm(n, mu, sd)
mean(x) # sample mean
var(x) # sample variance

n <- 10000
mu <- 10
sd <- 3
x <- rnorm(n, mu, sd)
hist(x)

# Density function - Synarthsh Pyknothtas f(y)
y <- 2
dnorm(y, mu, sd)

# Distribution function - Synarthsh Katanomhs P(Y<=y)
pnorm(y, mu, sd)

# Simulate from the Beta distribution
n <- 10000
x <- rbeta(n, 2, 4)
hist(x)